


<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  
  <title>torch.utils.checkpoint &mdash; PyTorch master documentation</title>
  

  
  
  
  
    <link rel="canonical" href="https://pytorch.org/docs/stable/_modules/torch/utils/checkpoint.html"/>
  

  

  
  
    

  

  <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
  <!-- <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" /> -->
  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css" type="text/css" />
  <link rel="stylesheet" href="../../../_static/css/jit.css" type="text/css" />
  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.11.1/dist/katex.min.css" type="text/css" />
  <link rel="stylesheet" href="../../../_static/katex-math.css" type="text/css" />
    <link rel="index" title="Index" href="../../../genindex.html" />
    <link rel="search" title="Search" href="../../../search.html" /> 

  
  <script src="../../../_static/js/modernizr.min.js"></script>

  <!-- Preload the theme fonts -->

<link rel="preload" href="../../../_static/fonts/FreightSans/freight-sans-book.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../_static/fonts/FreightSans/freight-sans-medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../_static/fonts/IBMPlexMono/IBMPlexMono-Medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../_static/fonts/FreightSans/freight-sans-bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../_static/fonts/FreightSans/freight-sans-medium-italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../_static/fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff2" as="font" type="font/woff2" crossorigin="anonymous">

<!-- Preload the katex fonts -->

<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Math-Italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Main-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Main-Bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size1-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size4-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size2-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size3-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Caligraphic-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
</head>

<div class="container-fluid header-holder tutorials-header" id="header-holder">
  <div class="container">
    <div class="header-container">
      <a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>

      <div class="main-menu">
        <ul>
          <li>
            <a href="https://pytorch.org/get-started">Get Started</a>
          </li>

          <li>
            <div class="ecosystem-dropdown">
              <a id="dropdownMenuButton" data-toggle="ecosystem-dropdown">
                Ecosystem
              </a>
              <div class="ecosystem-dropdown-menu">
                <a class="nav-dropdown-item" href="https://pytorch.org/hub"">
                  <span class=dropdown-title>Models (Beta)</span>
                  <p>Discover, publish, and reuse pre-trained models</p>
                </a>
                <a class="nav-dropdown-item" href="https://pytorch.org/ecosystem">
                  <span class=dropdown-title>Tools & Libraries</span>
                  <p>Explore the ecosystem of tools and libraries</p>
                </a>
              </div>
            </div>
          </li>

          <li>
            <a href="https://pytorch.org/mobile">Mobile</a>
          </li>

          <li>
            <a href="https://pytorch.org/blog/">Blog</a>
          </li>

          <li>
            <a href="https://pytorch.org/tutorials">Tutorials</a>
          </li>

          <li class="active">
            <a href="https://pytorch.org/docs/stable/index.html">Docs</a>
          </li>

          <li>
            <div class="resources-dropdown">
              <a id="resourcesDropdownButton" data-toggle="resources-dropdown">
                Resources
              </a>
              <div class="resources-dropdown-menu">
                <a class="nav-dropdown-item" href="https://pytorch.org/resources"">
                  <span class=dropdown-title>Developer Resources</span>
                  <p>Find resources and get questions answered</p>
                </a>
                <a class="nav-dropdown-item" href="https://pytorch.org/features">
                  <span class=dropdown-title>About</span>
                  <p>Learn about PyTorch’s features and capabilities</p>
                </a>
              </div>
            </div>
          </li>

          <li>
            <a href="https://github.com/pytorch/pytorch">Github</a>
          </li>
        </ul>
      </div>

      <a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
    </div>

  </div>
</div>


<body class="pytorch-body">

   

    

    <div class="table-of-contents-link-wrapper">
      <span>Table of Contents</span>
      <a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
    </div>

    <nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
      <div class="pytorch-side-scroll">
        <div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
          <div class="pytorch-left-menu-search">
            

            
              
              
                <div class="version">
                  master (1.5.0 )
                </div>
              
            

            


  


<div role="search">
  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
    <input type="text" name="q" placeholder="Search Docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>

            
          </div>

          
<div>
  <a style="color:#F05732" href="https://pytorch.org/docs/stable/_modules/torch/utils/checkpoint.html">
    You are viewing unstable developer preview docs.
    Click here to view docs for latest stable release.
  </a>
</div>

            
            
              
            
            
              <p class="caption"><span class="caption-text">Notes</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/cuda.html">CUDA semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/ddp.html">Distributed Data Parallel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/multiprocessing.html">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/randomness.html">Reproducibility</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/serialization.html">Serialization semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../notes/windows.html">Windows FAQ</a></li>
</ul>
<p class="caption"><span class="caption-text">Language Bindings</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/cppdocs/">C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../packages.html">Javadoc</a></li>
</ul>
<p class="caption"><span class="caption-text">Python API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../torch.html">torch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../nn.html">torch.nn</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../nn.functional.html">torch.nn.functional</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tensors.html">torch.Tensor</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tensor_attributes.html">Tensor Attributes</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tensor_view.html">Tensor Views</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../autograd.html">torch.autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../cuda.html">torch.cuda</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../amp.html">torch.cuda.amp</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../distributed.html">torch.distributed</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../distributions.html">torch.distributions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../hub.html">torch.hub</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../jit.html">torch.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../nn.init.html">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../onnx.html">torch.onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../optim.html">torch.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../rpc/index.html">Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../random.html">torch.random</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../sparse.html">torch.sparse</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../storage.html">torch.Storage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../bottleneck.html">torch.utils.bottleneck</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../checkpoint.html">torch.utils.checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../cpp_extension.html">torch.utils.cpp_extension</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../data.html">torch.utils.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../dlpack.html">torch.utils.dlpack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../model_zoo.html">torch.utils.model_zoo</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tensorboard.html">torch.utils.tensorboard</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../type_info.html">Type Info</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../named_tensor.html">Named Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../name_inference.html">Named Tensors operator coverage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../__config__.html">torch.__config__</a></li>
</ul>
<p class="caption"><span class="caption-text">Libraries</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/audio">torchaudio</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/text">torchtext</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/elastic/">TorchElastic</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/serve">TorchServe</a></li>
<li class="toctree-l1"><a class="reference external" href="http://pytorch.org/xla/">PyTorch on XLA Devices</a></li>
</ul>
<p class="caption"><span class="caption-text">Community</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../community/contribution_guide.html">PyTorch Contribution Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../community/governance.html">PyTorch Governance</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../community/persons_of_interest.html">PyTorch Governance | Persons of Interest</a></li>
</ul>

            
          

        </div>
      </div>
    </nav>

    <div class="pytorch-container">
      <div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
        <div class="pytorch-breadcrumbs-wrapper">
          















<div role="navigation" aria-label="breadcrumbs navigation">

  <ul class="pytorch-breadcrumbs">
    
      <li>
        <a href="../../../index.html">
          
            Docs
          
        </a> &gt;
      </li>

        
          <li><a href="../../index.html">Module code</a> &gt;</li>
        
          <li><a href="../../torch.html">torch</a> &gt;</li>
        
      <li>torch.utils.checkpoint</li>
    
    
      <li class="pytorch-breadcrumbs-aside">
        
      </li>
    
  </ul>

  
</div>
        </div>

        <div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
          Shortcuts
        </div>
      </div>

      <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
        <div class="pytorch-content-left">

        
          
          <div class="rst-content">
          
            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
              
  <h1>Source code for torch.utils.checkpoint</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">unicode_literals</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">warnings</span>


<span class="k">def</span> <span class="nf">detach_variable</span><span class="p">(</span><span class="n">inputs</span><span class="p">):</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
        <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">inp</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span>
                <span class="k">continue</span>

            <span class="n">x</span> <span class="o">=</span> <span class="n">inp</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
            <span class="n">x</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="n">inp</span><span class="o">.</span><span class="n">requires_grad</span>
            <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
            <span class="s2">&quot;Only tuple of tensors is supported. Got Unsupported input type: &quot;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">check_backward_validity</span><span class="p">(</span><span class="n">inputs</span><span class="p">):</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="n">inp</span><span class="o">.</span><span class="n">requires_grad</span> <span class="k">for</span> <span class="n">inp</span> <span class="ow">in</span> <span class="n">inputs</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)):</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;None of the inputs have requires_grad=True. Gradients will be None&quot;</span><span class="p">)</span>


<span class="c1"># We can&#39;t know if the run_fn will internally move some args to different devices,</span>
<span class="c1"># which would require logic to preserve rng states for those devices as well.</span>
<span class="c1"># We could paranoically stash and restore ALL the rng states for all visible devices,</span>
<span class="c1"># but that seems very wasteful for most cases.  Compromise:  Stash the RNG state for</span>
<span class="c1"># the device of all Tensor args.</span>
<span class="c1">#</span>
<span class="c1"># To consider:  maybe get_device_states and set_device_states should reside in torch/random.py?</span>
<span class="k">def</span> <span class="nf">get_device_states</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span>
    <span class="c1"># This will not error out if &quot;arg&quot; is a CPU tensor or a non-tensor type because</span>
    <span class="c1"># the conditionals short-circuit.</span>
    <span class="n">fwd_gpu_devices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">arg</span><span class="o">.</span><span class="n">get_device</span><span class="p">()</span> <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">args</span>
                               <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="n">arg</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">))</span>

    <span class="n">fwd_gpu_states</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">device</span> <span class="ow">in</span> <span class="n">fwd_gpu_devices</span><span class="p">:</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device</span><span class="p">):</span>
            <span class="n">fwd_gpu_states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_rng_state</span><span class="p">())</span>

    <span class="k">return</span> <span class="n">fwd_gpu_devices</span><span class="p">,</span> <span class="n">fwd_gpu_states</span>


<span class="k">def</span> <span class="nf">set_device_states</span><span class="p">(</span><span class="n">devices</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">device</span><span class="p">,</span> <span class="n">state</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">devices</span><span class="p">,</span> <span class="n">states</span><span class="p">):</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device</span><span class="p">):</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">set_rng_state</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>


<span class="k">class</span> <span class="nc">CheckpointFunction</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">run_function</span><span class="p">,</span> <span class="n">preserve_rng_state</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
        <span class="n">check_backward_validity</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
        <span class="n">ctx</span><span class="o">.</span><span class="n">run_function</span> <span class="o">=</span> <span class="n">run_function</span>
        <span class="n">ctx</span><span class="o">.</span><span class="n">preserve_rng_state</span> <span class="o">=</span> <span class="n">preserve_rng_state</span>
        <span class="k">if</span> <span class="n">preserve_rng_state</span><span class="p">:</span>
            <span class="n">ctx</span><span class="o">.</span><span class="n">fwd_cpu_state</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">get_rng_state</span><span class="p">()</span>
            <span class="c1"># Don&#39;t eagerly initialize the cuda context by accident.</span>
            <span class="c1"># (If the user intends that the context is initialized later, within their</span>
            <span class="c1"># run_function, we SHOULD actually stash the cuda state here.  Unfortunately,</span>
            <span class="c1"># we have no way to anticipate this will happen before we run the function.)</span>
            <span class="n">ctx</span><span class="o">.</span><span class="n">had_cuda_in_fwd</span> <span class="o">=</span> <span class="kc">False</span>
            <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">_initialized</span><span class="p">:</span>
                <span class="n">ctx</span><span class="o">.</span><span class="n">had_cuda_in_fwd</span> <span class="o">=</span> <span class="kc">True</span>
                <span class="n">ctx</span><span class="o">.</span><span class="n">fwd_gpu_devices</span><span class="p">,</span> <span class="n">ctx</span><span class="o">.</span><span class="n">fwd_gpu_states</span> <span class="o">=</span> <span class="n">get_device_states</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
        <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
            <span class="n">outputs</span> <span class="o">=</span> <span class="n">run_function</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">outputs</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">_is_checkpoint_valid</span><span class="p">():</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Checkpointing is not compatible with .grad(), please use .backward() if possible&quot;</span><span class="p">)</span>
        <span class="n">inputs</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
        <span class="c1"># Stash the surrounding rng state, and mimic the state that was</span>
        <span class="c1"># present at this time during forward.  Restore the surrounding state</span>
        <span class="c1"># when we&#39;re done.</span>
        <span class="n">rng_devices</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">if</span> <span class="n">ctx</span><span class="o">.</span><span class="n">preserve_rng_state</span> <span class="ow">and</span> <span class="n">ctx</span><span class="o">.</span><span class="n">had_cuda_in_fwd</span><span class="p">:</span>
            <span class="n">rng_devices</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">fwd_gpu_devices</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">fork_rng</span><span class="p">(</span><span class="n">devices</span><span class="o">=</span><span class="n">rng_devices</span><span class="p">,</span> <span class="n">enabled</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">preserve_rng_state</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">ctx</span><span class="o">.</span><span class="n">preserve_rng_state</span><span class="p">:</span>
                <span class="n">torch</span><span class="o">.</span><span class="n">set_rng_state</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">fwd_cpu_state</span><span class="p">)</span>
                <span class="k">if</span> <span class="n">ctx</span><span class="o">.</span><span class="n">had_cuda_in_fwd</span><span class="p">:</span>
                    <span class="n">set_device_states</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">fwd_gpu_devices</span><span class="p">,</span> <span class="n">ctx</span><span class="o">.</span><span class="n">fwd_gpu_states</span><span class="p">)</span>
            <span class="n">detached_inputs</span> <span class="o">=</span> <span class="n">detach_variable</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">enable_grad</span><span class="p">():</span>
                <span class="n">outputs</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">run_function</span><span class="p">(</span><span class="o">*</span><span class="n">detached_inputs</span><span class="p">)</span>

        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
            <span class="n">outputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">outputs</span><span class="p">,)</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">args</span><span class="p">)</span>
        <span class="n">grads</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">inp</span><span class="o">.</span><span class="n">grad</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">inp</span>
                      <span class="k">for</span> <span class="n">inp</span> <span class="ow">in</span> <span class="n">detached_inputs</span><span class="p">)</span>
        <span class="k">return</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="o">+</span> <span class="n">grads</span>


<div class="viewcode-block" id="checkpoint"><a class="viewcode-back" href="../../../checkpoint.html#torch.utils.checkpoint.checkpoint">[docs]</a><span class="k">def</span> <span class="nf">checkpoint</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Checkpoint a model or part of the model</span>

<span class="sd">    Checkpointing works by trading compute for memory. Rather than storing all</span>
<span class="sd">    intermediate activations of the entire computation graph for computing</span>
<span class="sd">    backward, the checkpointed part does **not** save intermediate activations,</span>
<span class="sd">    and instead recomputes them in backward pass. It can be applied on any part</span>
<span class="sd">    of a model.</span>

<span class="sd">    Specifically, in the forward pass, :attr:`function` will run in</span>
<span class="sd">    :func:`torch.no_grad` manner, i.e., not storing the intermediate</span>
<span class="sd">    activations. Instead, the forward pass saves the inputs tuple and the</span>
<span class="sd">    :attr:`function` parameter. In the backwards pass, the saved inputs and</span>
<span class="sd">    :attr:`function` is retrieved, and the forward pass is computed on</span>
<span class="sd">    :attr:`function` again, now tracking the intermediate activations, and then</span>
<span class="sd">    the gradients are calculated using these activation values.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        Checkpointing doesn&#39;t work with :func:`torch.autograd.grad`, but only</span>
<span class="sd">        with :func:`torch.autograd.backward`.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        If :attr:`function` invocation during backward does anything different</span>
<span class="sd">        than the one during forward, e.g., due to some global variable, the</span>
<span class="sd">        checkpointed version won&#39;t be equivalent, and unfortunately it can&#39;t be</span>
<span class="sd">        detected.</span>

<span class="sd">    .. warning:</span>
<span class="sd">        At least one of the inputs needs to have :code:`requires_grad=True` if</span>
<span class="sd">        grads are needed for model inputs, otherwise the checkpointed part of the</span>
<span class="sd">        model won&#39;t have gradients.</span>

<span class="sd">    Args:</span>
<span class="sd">        function: describes what to run in the forward pass of the model or</span>
<span class="sd">            part of the model. It should also know how to handle the inputs</span>
<span class="sd">            passed as the tuple. For example, in LSTM, if user passes</span>
<span class="sd">            ``(activation, hidden)``, :attr:`function` should correctly use the</span>
<span class="sd">            first input as ``activation`` and the second input as ``hidden``</span>
<span class="sd">        preserve_rng_state(bool, optional, default=True):  Omit stashing and restoring</span>
<span class="sd">            the RNG state during each checkpoint.</span>
<span class="sd">        args: tuple containing inputs to the :attr:`function`</span>

<span class="sd">    Returns:</span>
<span class="sd">        Output of running :attr:`function` on :attr:`*args`</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># Hack to mix *args with **kwargs in a python 2.7-compliant way</span>
    <span class="n">preserve</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;preserve_rng_state&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">kwargs</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unexpected keyword arguments: &quot;</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">arg</span> <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">))</span>

    <span class="k">return</span> <span class="n">CheckpointFunction</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="n">preserve</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span></div>


<div class="viewcode-block" id="checkpoint_sequential"><a class="viewcode-back" href="../../../checkpoint.html#torch.utils.checkpoint.checkpoint_sequential">[docs]</a><span class="k">def</span> <span class="nf">checkpoint_sequential</span><span class="p">(</span><span class="n">functions</span><span class="p">,</span> <span class="n">segments</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;A helper function for checkpointing sequential models.</span>

<span class="sd">    Sequential models execute a list of modules/functions in order</span>
<span class="sd">    (sequentially). Therefore, we can divide such a model in various segments</span>
<span class="sd">    and checkpoint each segment. All segments except the last will run in</span>
<span class="sd">    :func:`torch.no_grad` manner, i.e., not storing the intermediate</span>
<span class="sd">    activations. The inputs of each checkpointed segment will be saved for</span>
<span class="sd">    re-running the segment in the backward pass.</span>

<span class="sd">    See :func:`~torch.utils.checkpoint.checkpoint` on how checkpointing works.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        Checkpointing doesn&#39;t work with :func:`torch.autograd.grad`, but only</span>
<span class="sd">        with :func:`torch.autograd.backward`.</span>

<span class="sd">    .. warning:</span>
<span class="sd">        At least one of the inputs needs to have :code:`requires_grad=True` if</span>
<span class="sd">        grads are needed for model inputs, otherwise the checkpointed part of the</span>
<span class="sd">        model won&#39;t have gradients.</span>

<span class="sd">    .. warning:</span>
<span class="sd">        Since PyTorch 1.4, it allows only one Tensor as the input and</span>
<span class="sd">        intermediate outputs, just like :class:`torch.nn.Sequential`.</span>

<span class="sd">    Args:</span>
<span class="sd">        functions: A :class:`torch.nn.Sequential` or the list of modules or</span>
<span class="sd">            functions (comprising the model) to run sequentially.</span>
<span class="sd">        segments: Number of chunks to create in the model</span>
<span class="sd">        input: A Tensor that is input to :attr:`functions`</span>
<span class="sd">        preserve_rng_state(bool, optional, default=True):  Omit stashing and restoring</span>
<span class="sd">            the RNG state during each checkpoint.</span>

<span class="sd">    Returns:</span>
<span class="sd">        Output of running :attr:`functions` sequentially on :attr:`*inputs`</span>

<span class="sd">    Example:</span>
<span class="sd">        &gt;&gt;&gt; model = nn.Sequential(...)</span>
<span class="sd">        &gt;&gt;&gt; input_var = checkpoint_sequential(model, chunks, input_var)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># Hack for keyword-only parameter in a python 2.7-compliant way</span>
    <span class="n">preserve</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;preserve_rng_state&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">kwargs</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unexpected keyword arguments: &quot;</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">arg</span> <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">))</span>

    <span class="k">def</span> <span class="nf">run_function</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">functions</span><span class="p">):</span>
        <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
                <span class="nb">input</span> <span class="o">=</span> <span class="n">functions</span><span class="p">[</span><span class="n">j</span><span class="p">](</span><span class="nb">input</span><span class="p">)</span>
            <span class="k">return</span> <span class="nb">input</span>
        <span class="k">return</span> <span class="n">forward</span>

    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">functions</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
        <span class="n">functions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">functions</span><span class="o">.</span><span class="n">children</span><span class="p">())</span>

    <span class="n">segment_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">functions</span><span class="p">)</span> <span class="o">//</span> <span class="n">segments</span>
    <span class="c1"># the last chunk has to be non-volatile</span>
    <span class="n">end</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
    <span class="k">for</span> <span class="n">start</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">segment_size</span> <span class="o">*</span> <span class="p">(</span><span class="n">segments</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span> <span class="n">segment_size</span><span class="p">):</span>
        <span class="n">end</span> <span class="o">=</span> <span class="n">start</span> <span class="o">+</span> <span class="n">segment_size</span> <span class="o">-</span> <span class="mi">1</span>
        <span class="nb">input</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">(</span><span class="n">run_function</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">functions</span><span class="p">),</span> <span class="nb">input</span><span class="p">,</span>
                           <span class="n">preserve_rng_state</span><span class="o">=</span><span class="n">preserve</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">run_function</span><span class="p">(</span><span class="n">end</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">functions</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">functions</span><span class="p">)(</span><span class="nb">input</span><span class="p">)</span></div>
</pre></div>

             </article>
             
            </div>
            <footer>
  

  

    <hr>

  

  <div role="contentinfo">
    <p>
        &copy; Copyright 2019, Torch Contributors.

    </p>
  </div>
    
      <div>
        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
      </div>
     

</footer>

          </div>
        </div>

        <div class="pytorch-content-right" id="pytorch-content-right">
          <div class="pytorch-right-menu" id="pytorch-right-menu">
            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
              
            </div>
          </div>
        </div>
      </section>
    </div>

  


  

     
       <script type="text/javascript" id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
         <script src="../../../_static/jquery.js"></script>
         <script src="../../../_static/underscore.js"></script>
         <script src="../../../_static/doctools.js"></script>
         <script src="../../../_static/language_data.js"></script>
     

  

  <script type="text/javascript" src="../../../_static/js/vendor/popper.min.js"></script>
  <script type="text/javascript" src="../../../_static/js/vendor/bootstrap.min.js"></script>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/list.js/1.5.0/list.min.js"></script>
  <script type="text/javascript" src="../../../_static/js/theme.js"></script>

  <script type="text/javascript">
      jQuery(function () {
          SphinxRtdTheme.Navigation.enable(true);
      });
  </script>
 
<script>
  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');

  ga('create', 'UA-90545585-1', 'auto');
  ga('send', 'pageview');

</script>

<script async src="https://www.googletagmanager.com/gtag/js?id=UA-117752657-2"></script>

<script>
  window.dataLayer = window.dataLayer || [];

  function gtag(){dataLayer.push(arguments);}

  gtag('js', new Date());
  gtag('config', 'UA-117752657-2');
</script>

<img height="1" width="1" style="border-style:none;" alt="" src="https://www.googleadservices.com/pagead/conversion/795629140/?label=txkmCPmdtosBENSssfsC&amp;guid=ON&amp;script=0"/>


  <!-- Begin Footer -->

  <div class="container-fluid docs-tutorials-resources" id="docs-tutorials-resources">
    <div class="container">
      <div class="row">
        <div class="col-md-4 text-center">
          <h2>Docs</h2>
          <p>Access comprehensive developer documentation for PyTorch</p>
          <a class="with-right-arrow" href="https://pytorch.org/docs/stable/index.html">View Docs</a>
        </div>

        <div class="col-md-4 text-center">
          <h2>Tutorials</h2>
          <p>Get in-depth tutorials for beginners and advanced developers</p>
          <a class="with-right-arrow" href="https://pytorch.org/tutorials">View Tutorials</a>
        </div>

        <div class="col-md-4 text-center">
          <h2>Resources</h2>
          <p>Find development resources and get your questions answered</p>
          <a class="with-right-arrow" href="https://pytorch.org/resources">View Resources</a>
        </div>
      </div>
    </div>
  </div>

  <footer class="site-footer">
    <div class="container footer-container">
      <div class="footer-logo-wrapper">
        <a href="https://pytorch.org/" class="footer-logo"></a>
      </div>

      <div class="footer-links-wrapper">
        <div class="footer-links-col">
          <ul>
            <li class="list-title"><a href="https://pytorch.org/">PyTorch</a></li>
            <li><a href="https://pytorch.org/get-started">Get Started</a></li>
            <li><a href="https://pytorch.org/features">Features</a></li>
            <li><a href="https://pytorch.org/ecosystem">Ecosystem</a></li>
            <li><a href="https://pytorch.org/blog/">Blog</a></li>
            <li><a href="https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md">Contributing</a></li>
          </ul>
        </div>

        <div class="footer-links-col">
          <ul>
            <li class="list-title"><a href="https://pytorch.org/resources">Resources</a></li>
            <li><a href="https://pytorch.org/tutorials">Tutorials</a></li>
            <li><a href="https://pytorch.org/docs/stable/index.html">Docs</a></li>
            <li><a href="https://discuss.pytorch.org" target="_blank">Discuss</a></li>
            <li><a href="https://github.com/pytorch/pytorch/issues" target="_blank">Github Issues</a></li>
            <li><a href="https://pytorch.org/assets/brand-guidelines/PyTorch-Brand-Guidelines.pdf" target="_blank">Brand Guidelines</a></li>
          </ul>
        </div>

        <div class="footer-links-col follow-us-col">
          <ul>
            <li class="list-title">Stay Connected</li>
            <li>
              <div id="mc_embed_signup">
                <form
                  action="https://twitter.us14.list-manage.com/subscribe/post?u=75419c71fe0a935e53dfa4a3f&id=91d0dccd39"
                  method="post"
                  id="mc-embedded-subscribe-form"
                  name="mc-embedded-subscribe-form"
                  class="email-subscribe-form validate"
                  target="_blank"
                  novalidate>
                  <div id="mc_embed_signup_scroll" class="email-subscribe-form-fields-wrapper">
                    <div class="mc-field-group">
                      <label for="mce-EMAIL" style="display:none;">Email Address</label>
                      <input type="email" value="" name="EMAIL" class="required email" id="mce-EMAIL" placeholder="Email Address">
                    </div>

                    <div id="mce-responses" class="clear">
                      <div class="response" id="mce-error-response" style="display:none"></div>
                      <div class="response" id="mce-success-response" style="display:none"></div>
                    </div>    <!-- real people should not fill this in and expect good things - do not remove this or risk form bot signups-->

                    <div style="position: absolute; left: -5000px;" aria-hidden="true"><input type="text" name="b_75419c71fe0a935e53dfa4a3f_91d0dccd39" tabindex="-1" value=""></div>

                    <div class="clear">
                      <input type="submit" value="" name="subscribe" id="mc-embedded-subscribe" class="button email-subscribe-button">
                    </div>
                  </div>
                </form>
              </div>

            </li>
          </ul>

          <div class="footer-social-icons">
            <a href="https://www.facebook.com/pytorch" target="_blank" class="facebook"></a>
            <a href="https://twitter.com/pytorch" target="_blank" class="twitter"></a>
            <a href="https://www.youtube.com/pytorch" target="_blank" class="youtube"></a>
          </div>
        </div>
      </div>
    </div>
  </footer>

  <div class="cookie-banner-wrapper">
  <div class="container">
    <p class="gdpr-notice">To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: <a href="https://www.facebook.com/policies/cookies/">Cookies Policy</a>.</p>
    <img class="close-button" src="../../../_static/images/pytorch-x.svg">
  </div>
</div>

  <!-- End Footer -->

  <!-- Begin Mobile Menu -->

  <div class="mobile-main-menu">
    <div class="container-fluid">
      <div class="container">
        <div class="mobile-main-menu-header-container">
          <a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
          <a class="main-menu-close-button" href="#" data-behavior="close-mobile-menu"></a>
        </div>
      </div>
    </div>

    <div class="mobile-main-menu-links-container">
      <div class="main-menu">
        <ul>
          <li>
            <a href="https://pytorch.org/get-started">Get Started</a>
          </li>

          <li>
            <a href="https://pytorch.org/features">Features</a>
          </li>

          <li>
            <a href="https://pytorch.org/ecosystem">Ecosystem</a>
          </li>

          <li>
            <a href="https://pytorch.org/mobile">Mobile</a>
          </li>

          <li>
            <a href="https://pytorch.org/hub">PyTorch Hub</a>
          </li>

          <li>
            <a href="https://pytorch.org/blog/">Blog</a>
          </li>

          <li>
            <a href="https://pytorch.org/tutorials">Tutorials</a>
          </li>

          <li class="active">
            <a href="https://pytorch.org/docs/stable/index.html">Docs</a>
          </li>

          <li>
            <a href="https://pytorch.org/resources">Resources</a>
          </li>

          <li>
            <a href="https://github.com/pytorch/pytorch">Github</a>
          </li>
        </ul>
      </div>
    </div>
  </div>

  <!-- End Mobile Menu -->

  <script type="text/javascript" src="../../../_static/js/vendor/anchor.min.js"></script>

  <script type="text/javascript">
    $(document).ready(function() {
      mobileMenu.bind();
      mobileTOC.bind();
      pytorchAnchors.bind();
      sideMenus.bind();
      scrollToAnchor.bind();
      highlightNavigation.bind();
      mainMenuDropdown.bind();
      filterTags.bind();

      // Remove any empty p tags that Sphinx adds
      $("[data-tags='null']").remove();

      // Add class to links that have code blocks, since we cannot create links in code blocks
      $("article.pytorch-article a span.pre").each(function(e) {
        $(this).closest("a").addClass("has-code");
      });
    })
  </script>
</body>
</html>